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Automatic Fusion of Hyperspectral Images and Laser Scans Using Feature Points
Joint Authors
Zhang, Xiao
Zhang, Aiwu
Meng, Xiangang
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Automatic fusion of different kinds of image datasets is so intractable with diverse imaging principle.
This paper presents a novel method for automatic fusion of two different images: 2D hyperspectral images acquired with a hyperspectral camera and 3D laser scans obtained with a laser scanner, without any other sensor.
Only a few corresponding feature points are used, which are automatically extracted from a scene viewed by the two sensors.
Extraction method of feature points relies on SURF algorithm and camera model, which can convert a 3D laser scan into a 2D laser image with the intensity of the pixels defined by the attributes in the laser scan.
Moreover, Collinearity Equation and Direct Linear Transformation are used to create the initial corresponding relationship of the two images.
Adjustment is also used to create corrected values to eliminate errors.
The experimental result shows that this method is successfully validated with images collected by a hyperspectral camera and a laser scanner.
American Psychological Association (APA)
Zhang, Xiao& Zhang, Aiwu& Meng, Xiangang. 2015. Automatic Fusion of Hyperspectral Images and Laser Scans Using Feature Points. Journal of Sensors،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1070116
Modern Language Association (MLA)
Zhang, Xiao…[et al.]. Automatic Fusion of Hyperspectral Images and Laser Scans Using Feature Points. Journal of Sensors No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1070116
American Medical Association (AMA)
Zhang, Xiao& Zhang, Aiwu& Meng, Xiangang. Automatic Fusion of Hyperspectral Images and Laser Scans Using Feature Points. Journal of Sensors. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1070116
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070116